Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we will classify mushrooms as being edible or poisonous depending on different features. Logistic regression, linear discriminant analysis and quadratic discriminant analysis will be used.\n",
    "\n",
    "The data set contains 8124 rows and the following features:\n",
    "\n",
    "`class`: edible(e) or poisonous(p)\n",
    "\n",
    "`cap-shape`: bell(b), conical(c), convex(x), flat(f), knobbed(k), sunken(s)\n",
    "\n",
    "`cap-surface`: fibrous(f), grooves(g), scaly(y), smooth(s)\n",
    "\n",
    "`cap-color`: brown(n), buff(b), cinnamon(c), gray(g), green(r), pink(p), purple(u), red(e), white(w), yellow(y)\n",
    "\n",
    "`bruises`: bruises(t), no bruises(f)\n",
    "\n",
    "`odor`: almond(a), anise(l), creosote(c), fishy(y), foul(f), musty(m), none(n), pungent(p), spicy(s)\n",
    "\n",
    "`gill-attachment`: attached(a), descending(d), free(f), notched(n)\n",
    "\n",
    "`gill-spacing`: close(c), crowded(w), distant(d)\n",
    "\n",
    "`gill-size`: broad(b), narrow(n)\n",
    "\n",
    "`gill-color`: black(k), brown(n), buff(b), chocolate(h), gray(g), green(r), orange(o), pink(p), purple(u), red(e), white(w), yellow(y)\n",
    "\n",
    "`stalk-shape`: enlarging(e), tapering(t)\n",
    "\n",
    "`stalk-root`: bulbous(b), club(c), cup(u), equal(e), rhizomorphs(z), rooted(r), missing(?)\n",
    "\n",
    "`stalk-surface-above-ring`: fibrous(f), scaly(y), silky(k), smooth(s)\n",
    "\n",
    "`stalk-surface-below-ring`: fibrous(f), scaly(y), silky(k), smooth(s)\n",
    "\n",
    "`stalk-color-above-ring`: brown(n), buff(b), cinnamon(c), gray(g), orange(o), pink(p), red(e), white(w), yellow(y)\n",
    "\n",
    "`stalk-color-below-ring`: brown(n), buff(b), cinnamon(c), gray(g), orange(o), pink(p), red(e), white(w), yellow(y)\n",
    "\n",
    "`veil-type`: partial(p), universal(u)\n",
    "\n",
    "`veil-color`: brown(n), orange(o), white(w), yellow(y)\n",
    "\n",
    "`ring-number`: none(n), one(o), two(t)\n",
    "\n",
    "`ring-type`: cobwebby(c), evanescent(e), flaring(f), large(l), none(n), pendant(p), sheathing(s), zone(z)\n",
    "\n",
    "`spore-print-color`: black(k), brown(n), buff(b), chocolate(h), green(r), orange(o), purple(u), white(w), yellow(y)\n",
    "\n",
    "`population`: abundant(a), clustered(c), numerous(n), scattered(s), several(v), solitary(y)\n",
    "\n",
    "`habitat`: grasses(g), leaves(l), meadows(m), paths(p), urban(u), waste(w), woods(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import roc_curve, auc, confusion_matrix\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>e</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>p</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>e</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0     p         x           s         n       t    p               f   \n",
       "1     e         x           s         y       t    a               f   \n",
       "2     e         b           s         w       t    l               f   \n",
       "3     p         x           y         w       t    p               f   \n",
       "4     e         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "0            c         n          k  ...                        s   \n",
       "1            c         b          k  ...                        s   \n",
       "2            c         b          n  ...                        s   \n",
       "3            c         n          n  ...                        s   \n",
       "4            w         b          k  ...                        s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATAPATH = 'data/mushrooms.csv'\n",
    "\n",
    "data = pd.read_csv(DATAPATH)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = data['class']\n",
    "ax = sns.countplot(x=x, data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data(hue, data):\n",
    "    for i, col in enumerate(data.columns):\n",
    "        plt.figure(i)\n",
    "        ax = sns.countplot(x=data[col], hue=hue, data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class: 0\n",
      "cap-shape: 0\n",
      "cap-surface: 0\n",
      "cap-color: 0\n",
      "bruises: 0\n",
      "odor: 0\n",
      "gill-attachment: 0\n",
      "gill-spacing: 0\n",
      "gill-size: 0\n",
      "gill-color: 0\n",
      "stalk-shape: 0\n",
      "stalk-root: 0\n",
      "stalk-surface-above-ring: 0\n",
      "stalk-surface-below-ring: 0\n",
      "stalk-color-above-ring: 0\n",
      "stalk-color-below-ring: 0\n",
      "veil-type: 0\n",
      "veil-color: 0\n",
      "ring-number: 0\n",
      "ring-type: 0\n",
      "spore-print-color: 0\n",
      "population: 0\n",
      "habitat: 0\n"
     ]
    }
   ],
   "source": [
    "for col in data.columns:\n",
    "    print(f\"{col}: {data[col].isnull().sum()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape</th>\n",
       "      <th>cap-surface</th>\n",
       "      <th>cap-color</th>\n",
       "      <th>bruises</th>\n",
       "      <th>odor</th>\n",
       "      <th>gill-attachment</th>\n",
       "      <th>gill-spacing</th>\n",
       "      <th>gill-size</th>\n",
       "      <th>gill-color</th>\n",
       "      <th>...</th>\n",
       "      <th>stalk-surface-below-ring</th>\n",
       "      <th>stalk-color-above-ring</th>\n",
       "      <th>stalk-color-below-ring</th>\n",
       "      <th>veil-type</th>\n",
       "      <th>veil-color</th>\n",
       "      <th>ring-number</th>\n",
       "      <th>ring-type</th>\n",
       "      <th>spore-print-color</th>\n",
       "      <th>population</th>\n",
       "      <th>habitat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>n</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>y</td>\n",
       "      <td>t</td>\n",
       "      <td>a</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>l</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>b</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>x</td>\n",
       "      <td>y</td>\n",
       "      <td>w</td>\n",
       "      <td>t</td>\n",
       "      <td>p</td>\n",
       "      <td>f</td>\n",
       "      <td>c</td>\n",
       "      <td>n</td>\n",
       "      <td>n</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>p</td>\n",
       "      <td>k</td>\n",
       "      <td>s</td>\n",
       "      <td>u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>x</td>\n",
       "      <td>s</td>\n",
       "      <td>g</td>\n",
       "      <td>f</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>w</td>\n",
       "      <td>b</td>\n",
       "      <td>k</td>\n",
       "      <td>...</td>\n",
       "      <td>s</td>\n",
       "      <td>w</td>\n",
       "      <td>w</td>\n",
       "      <td>p</td>\n",
       "      <td>w</td>\n",
       "      <td>o</td>\n",
       "      <td>e</td>\n",
       "      <td>n</td>\n",
       "      <td>a</td>\n",
       "      <td>g</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   class cap-shape cap-surface cap-color bruises odor gill-attachment  \\\n",
       "0      1         x           s         n       t    p               f   \n",
       "1      0         x           s         y       t    a               f   \n",
       "2      0         b           s         w       t    l               f   \n",
       "3      1         x           y         w       t    p               f   \n",
       "4      0         x           s         g       f    n               f   \n",
       "\n",
       "  gill-spacing gill-size gill-color  ... stalk-surface-below-ring  \\\n",
       "0            c         n          k  ...                        s   \n",
       "1            c         b          k  ...                        s   \n",
       "2            c         b          n  ...                        s   \n",
       "3            c         n          n  ...                        s   \n",
       "4            w         b          k  ...                        s   \n",
       "\n",
       "  stalk-color-above-ring stalk-color-below-ring veil-type veil-color  \\\n",
       "0                      w                      w         p          w   \n",
       "1                      w                      w         p          w   \n",
       "2                      w                      w         p          w   \n",
       "3                      w                      w         p          w   \n",
       "4                      w                      w         p          w   \n",
       "\n",
       "  ring-number ring-type spore-print-color population habitat  \n",
       "0           o         p                 k          s       u  \n",
       "1           o         p                 n          n       g  \n",
       "2           o         p                 n          n       m  \n",
       "3           o         p                 k          s       u  \n",
       "4           o         e                 n          a       g  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "data['class'] = le.fit_transform(data['class'])\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>cap-shape_b</th>\n",
       "      <th>cap-shape_c</th>\n",
       "      <th>cap-shape_f</th>\n",
       "      <th>cap-shape_k</th>\n",
       "      <th>cap-shape_s</th>\n",
       "      <th>cap-shape_x</th>\n",
       "      <th>cap-surface_f</th>\n",
       "      <th>cap-surface_g</th>\n",
       "      <th>cap-surface_s</th>\n",
       "      <th>...</th>\n",
       "      <th>population_s</th>\n",
       "      <th>population_v</th>\n",
       "      <th>population_y</th>\n",
       "      <th>habitat_d</th>\n",
       "      <th>habitat_g</th>\n",
       "      <th>habitat_l</th>\n",
       "      <th>habitat_m</th>\n",
       "      <th>habitat_p</th>\n",
       "      <th>habitat_u</th>\n",
       "      <th>habitat_w</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 118 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   class  cap-shape_b  cap-shape_c  cap-shape_f  cap-shape_k  cap-shape_s  \\\n",
       "0      1            0            0            0            0            0   \n",
       "1      0            0            0            0            0            0   \n",
       "2      0            1            0            0            0            0   \n",
       "3      1            0            0            0            0            0   \n",
       "4      0            0            0            0            0            0   \n",
       "\n",
       "   cap-shape_x  cap-surface_f  cap-surface_g  cap-surface_s  ...  \\\n",
       "0            1              0              0              1  ...   \n",
       "1            1              0              0              1  ...   \n",
       "2            0              0              0              1  ...   \n",
       "3            1              0              0              0  ...   \n",
       "4            1              0              0              1  ...   \n",
       "\n",
       "   population_s  population_v  population_y  habitat_d  habitat_g  habitat_l  \\\n",
       "0             1             0             0          0          0          0   \n",
       "1             0             0             0          0          1          0   \n",
       "2             0             0             0          0          0          0   \n",
       "3             1             0             0          0          0          0   \n",
       "4             0             0             0          0          1          0   \n",
       "\n",
       "   habitat_m  habitat_p  habitat_u  habitat_w  \n",
       "0          0          0          1          0  \n",
       "1          0          0          0          0  \n",
       "2          1          0          0          0  \n",
       "3          0          0          1          0  \n",
       "4          0          0          0          0  \n",
       "\n",
       "[5 rows x 118 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_data = pd.get_dummies(data)\n",
    "encoded_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data['class'].values.reshape(-1, 1)\n",
    "X = encoded_data.drop(['class'], axis=1)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "logistic_reg = LogisticRegression()\n",
    "\n",
    "logistic_reg.fit(X_train, y_train.ravel())\n",
    "\n",
    "y_prob = logistic_reg.predict_proba(X_test)[:,1]\n",
    "y_pred = np.where(y_prob > 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[843,   0],\n",
       "       [  0, 782]], dtype=int64)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "log_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob)\n",
    "roc_auc = auc(false_positive_rate, true_positive_rate)\n",
    "roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc(roc_auc):\n",
    "    plt.figure(figsize=(7,7))\n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.plot(false_positive_rate, true_positive_rate, color='red', label='AUC = %0.2f' % roc_auc)\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.plot([0, 1], [0, 1], linestyle='--')\n",
    "    plt.axis('tight')\n",
    "    plt.ylabel('True positive rate')\n",
    "    plt.xlabel('False positive rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(roc_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Discriminant Analysis (LDA) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:388: UserWarning: Variables are collinear.\n",
      "  warnings.warn(\"Variables are collinear.\")\n"
     ]
    }
   ],
   "source": [
    "lda = LinearDiscriminantAnalysis()\n",
    "\n",
    "lda.fit(X_train, y_train.ravel())\n",
    "\n",
    "y_prob_lda = lda.predict_proba(X_test)[:,1]\n",
    "y_pred_lda = np.where(y_prob_lda > 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[843,   0],\n",
       "       [  0, 782]], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda_confusion_matrix = confusion_matrix(y_test, y_pred_lda)\n",
    "lda_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob_lda)\n",
    "roc_auc_lda = auc(false_positive_rate, true_positive_rate)\n",
    "roc_auc_lda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(roc_auc_lda)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quadratic Discriminant Analysis (QDA) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\discriminant_analysis.py:693: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n"
     ]
    }
   ],
   "source": [
    "qda = QuadraticDiscriminantAnalysis()\n",
    "\n",
    "qda.fit(X_train, y_train.ravel())\n",
    "\n",
    "y_prob_qda = qda.predict_proba(X_test)[:,1]\n",
    "y_pred_qda = np.where(y_prob_qda > 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[843,   0],\n",
       "       [  0, 782]], dtype=int64)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qda_confusion_matrix = confusion_matrix(y_test, y_pred_qda)\n",
    "qda_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "false_positive_rate, true_positive_rate, thresholds = roc_curve(y_test, y_prob_qda)\n",
    "roc_auc_qda = auc(false_positive_rate, true_positive_rate)\n",
    "roc_auc_qda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(roc_auc_qda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}